Verification of a New Spatial Distribution Function of Soil Water Storage Capacity Using Conceptual and SWAT Models

被引:11
作者
Xie, Kang [1 ]
Liu, Pan [1 ]
Zhang, Jianyun [2 ]
Libera, Dominic A. [3 ]
Wang, Guoqing [2 ]
Li, Zejun [1 ]
Wang, Dingbao [3 ]
机构
[1] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
[2] Nanjing Hydraul Res Inst, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210029, Peoples R China
[3] Univ Cent Florida, Dept Civil Environm & Construct Engn, Orlando, FL 32816 USA
基金
中国国家自然科学基金;
关键词
Soil conservation service curve number (SCS-CN) model; Soil and water assessment tool (SWAT) model; Distribution function; Akaike information criterion; SCS-CN METHOD; GLOBAL OPTIMIZATION; CURVE NUMBER; RUNOFF; CALIBRATION; MOISTURE; SIMULATION; SCALE; PARAMETERIZATION; GENERATION;
D O I
10.1061/(ASCE)HE.1943-5584.0001887
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The Soil Conservation Service Curve Number (SCS-CN) method is widely used in conceptual rainfall-runoff models for describing the runoff response with a curve, which is a function of the cumulative storm rainfall and antecedent wetness conditions. To improve the SCS-CN method, a new distribution function was recently proposed to unify the surface runoff modeling of the SCS-CN method and probability-distributed functions in the variable infiltration capacity (VIC) and Xin'anjiang models. This study aims to verify the new distribution function in a conceptual rainfall-runoff model and in the Soil and Water Assessment Tool (SWAT) by using real catchments. The Xunhe River basin in China and other basins in the United States were used as case studies. Results show that more observed variability in streamflow is captured when using the new spatial distribution function of soil water storage capacity in the conceptual runoff model. Specifically, there is a 9.8% average increase in the Nash-Sutcliffe efficiency (NSE), while simultaneously reducing the bias and mean relative absolute error (MRAE). When using the new distribution in SWAT, the model is able to better estimate the observed streamflow as indicated by higher NSE values for most of the basins. Akaike information criterion (AIC) is used for validating the goodness-of-fit when the number of parameters and model structure change. Further findings suggest that the estimated variance is more sensitive to the value of the new shape parameter a when soil water content is low in the early stage of rainfall. Therefore, the proposed new distribution function is shown to be effective in improving the accuracy of simulating streamflow for both conceptual and SWAT models.
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页数:17
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